Towards Multi-Object Nonprehensile Transportation via Shared Teleoperation: A Framework Based on Virtual Object Model Predictive Control

TL;DR

This paper introduces a shared teleoperation framework for multi-object nonprehensile transportation, combining human control with autonomous robot orientation management using a virtual object model predictive control approach.

Contribution

It presents a novel VO-based dynamic constraint analysis and an MPC algorithm for real-time trajectory smoothing and orientation control in shared teleoperation.

Findings

Successfully manipulated nine objects with accelerations up to 2.4 m/s2.

Reduced sliding distance by 72.45% compared to baseline.

Achieved 0% tip-overs, improving robustness in complex scenarios.

Abstract

Multi-object nonprehensile transportation in teleoperation demands simultaneous trajectory tracking and tray orientation control. Existing methods often struggle with model dependency, uncertain parameters, and multi-object adaptability. We propose a shared teleoperation framework where humans and robots share positioning control, while the robot autonomously manages orientation to satisfy dynamic constraints. Key contributions include: 1) A theoretical dynamic constraint analysis utilizing a novel virtual object (VO)-based method to simplify constraints for trajectory planning. 2) An MPC-based trajectory smoothing algorithm that enforces real-time constraints and coordinates user tracking with orientation control. 3) Validations demonstrating stable manipulation of nine objects at accelerations up to 2.4 m/s2. Compared to the baseline, our approach reduces sliding distance by 72.45% and eliminates tip-overs (0% vs. 13.9%), proving robust adaptability in complex scenarios.